Nowadays cities invest more in their public services, and particularly digital ones, to improve their resident's quality of life and attract more people. Thus, new crowdsourcing services appear and they are based on contributions made by mobile users equipped with smartphones. For example, the respect of the traffic code is essential to ensure citizens' security and welfare in their city. In this paper, we present CrowdOut, a new mobile crowdsourcing service for improving road safety in cities. CrowdOut allows users to report traffic offence they witness in real time and to map them on a city plan. CrowdOut service has been implemented and experiments and demonstrations have been performed in the urban environment of the Grand Nancy, in France. This service allows users appropriating their urban environment with an active participation regarding the collectivity. This service also represents a tool for city administrators to help for decisions and improve their urbanization policy, or to check the impact of their policy in the city environment.

CrowdOut: A mobile crowdsourcing service for road safety in digital cities

Information about vehicle safety, such as the driving safety status and the road safety index, is of great importance to protect humans and support safe driving route planning. Despite some research on driving safety analysis, the accuracy and granularity of driving safety assessment are both very limited. Also, the problem of precisely and dynamically predicting road safety throughout a city has not been sufficiently studied and remains open. With the proliferation of sensor-equipped vehicles and smart devices, a huge amount of mobile sensing data provides an opportunity to conduct vehicle safety analysis. In this article, we first discuss mobile sensing data collection in VANETs and then identify two main challengs in vehicle safety analysis in VANETs, i.e., driving safety analysis and road safety analysis. In each issue, we review and classify the state-of-theart vehicle safety analysis techniques into different categories. For each category, a short description is given followed by a discussion of limitations. In order to improve vehicle safety, we propose a new deep learning framework (DeepRSI) to conduct real-time road safety prediction from the data mining perspective. Specifically, the proposed framework considers the spatio-temporal relationship of vehicle GPS trajectories and external environment factors. The evaluation results demonstrate the advantages of our proposed scheme over other methods by utilizing mobile sensing data collected in VANETs.

Vehicle Safety Improvement through Deep Learning and Mobile Sensing

Accurate maps are increasingly important with the growth of smart phones and the development of location-based services. Several crowdsourcing based map generation protocols that rely on users to provide their traces have been proposed. Being creative, however, those methods pose a significant threat to user privacy as the traces can easily imply user behavior patterns. On the flip side, crowdsourcing-based map generation method does need individual locations. To address the issue, we present a systematic participatory-sensing-based high-quality map generation scheme, PMG, that meets the privacy demand of individual users. To be specific, the individual users merely need to upload unorganized sparse location points to reduce the risk of exposing users’ traces and utilize the Crust , a technique from computational geometry for curve reconstruction, to estimate the unobserved map as well as evaluate the degree of privacy leakage. Experiments show that our solution is able to generate high-quality maps for a real environment that is robust to noisy data. The difference between the ground-truth map and the produced map is less than 10 m, even when the collected locations are about 32 m apart after clustering for the purpose of removing noise.

Privacy-Aware High-Quality Map Generation with Participatory Sensing

This paper contributes to mobile crowdsourcing applications by developing a privacy preserving framework that enables users to contribute content to the community while controlling their privacy exposure. One fundamental challenge in such applications is how to preserve user privacy, as participants may end up revealing a great deal of user-identified, geo-located data, which can easily unfold user trajectories or sensitive locations (e.g., user's home or work location). In this paper we develop PROMPT, a highly efficient privacy preserving framework that runs locally on mobile devices. PROMPT relies on a novel geometric approximation approach to preserve user privacy, by evaluating the privacy exposure of users before sharing their geo-located data. Our detailed experimental evaluation using real-world datasets illustrates that our approach is effective, practical and has low overhead on smartphones.

Location privacy for crowdsourcing applications

Participatory sensing, leveraging on the ubiquity of cheap sensors in mobile devices, enables various promising applications of great social benefit. However, its ubiquitous sampling and openness results in serious privacy concerns. People's activity trajectories may reveal their private information such as home and work place and thus requires proper protection. In this paper, we propose a novel design scheme, called query logics detached storage (QLDS), for trajectory privacy protection. The core idea of QLDS is to extract the query logics for personal trajectory retrieval and make actual trajectory tuples not clustered to any route-identity or user-identity at server end, which introduces fine-granularity anonymity. The QLDS design scheme stores the extracted query logics at users own client devices and the un-clustered location tuples at the backend server, guaranteeing trajectory reconstruction at client-end and privacy preservation at server-end. Besides, integration of QLDS with other privacy protection approaches can further enhance the protection strength and bring flexible configurations to meet individuals’ variable privacy concerns. The theoretical analytics is provided for the privacy and utility evaluation. The data retrieval performance of QLDS is experimentally evaluated in real-world internet environment.

Christian Roßkopf

Papers

uSafe: A privacy-aware and participative mobile application for citizen safety in urban environments